This invention relates to signal processing methods for spectral generation in radar data, and in particular, to spectral generation methods having noise reduction properties for radar data.
High Frequency Surface Wave Radar (HFSWR) is effective for the continuous detection and tracking of ships, aircraft, icebergs and other surface targets from a shore based location. HFSWR is currently being used to enhance search and rescue activities as well as to monitor sea state, illegal immigration, drug trafficking, illegal fishing, smuggling and piracy.
An HFSWR system is installed along a coastal line and includes a directional transmitting antenna that is directed towards the ocean and a directional receiving antenna array that is directed towards the ocean, as well as the hardware and software needed for system operation. The transmitting antenna generates a train of electromagnetic (EM) pulses which illuminate a desired surveillance area. The receiving antenna array is calibrated to have equal gain and phase over the entire surveillance area. Objects in the surveillance area then reflect the EM pulses towards the receiving antenna array which collects radar data. Some of the objects may be elements that must be detected (the radar signatures from these elements are referred to as xe2x80x9ctargetsxe2x80x9d) while the rest of the objects are elements that do not have to be detected (the radar signatures from these elements are referred to as xe2x80x9cclutterxe2x80x9d which is one type of noise in a radar system). More sophisticated pulse-coded or frequency-coded EM pulses may be used to combat range-wrap which occurs when a reflected EM pulse (in response to a previously transmitted EM pulse) is received by the receiving antenna array after subsequent EM pulses have been transmitted.
Conventionally, the radar data collected from each antenna element or sensor in the receiving antenna array is preprocessed by passing the data through a bandpass filter to filter extraneous unwanted signals in the radar data, and then through a heterodyne receiver which demodulates the radar data from the RF band to an IF band where analog to digital conversion occurs. The radar data is then demodulated to the baseband where low-pass filtering and downsampling occurs. The radar data collected by the receiving antenna array is complex (i.e. has real and imaginary components). Accordingly, the downsampled radar data is also complex and each of the signal processing components required to perform the above-mentioned operations are implemented to handle complex data.
The downsampled radar data is then processed by a matched filter that has a transfer function or impulse response that is related to the transmitted EM pulse. The matched filtered radar data is then separated into segments for analysis. Each segment is known in the art as a coherent integration time (CIT) or a dwell. The matched filtered radar data in each CIT is range-aligned by noting the time at which each data point was sampled relative to the time that a preceding EM pulse was transmitted. The range-aligned data may then be subjected to a combination of low-pass filtering for further noise reduction and downsampling for more efficient signal processing. The output of this processing is a plurality of time samples of range data where each time sample series corresponds to a range value. The maximum range value for which the plurality of time series is collected depends on the pulse repetition interval used in transmitting the EM pulses (i.e. the frequency at which EM pulses are transmitted).
A target is detected from range, doppler and azimuth information that is generated from the preprocessed recorded radar data. The range information is used to provide an estimate of the targets distance from the receiving antenna array. The azimuth information is used to provide an estimate of the angle of the target""s location with respect to the center of the receiving antenna array, and the doppler information is used to provide an estimate of the target""s radial velocity by measuring the target""s doppler shift. The target""s doppler shift is related to the change in frequency content of the EM pulse that is reflected by the target with respect to the original frequency content of that EM pulse.
As mentioned previously, range data is generated by noting the time at which data is sampled relative to the time at which a preceding EM pulse is transmitted. Doppler processing corresponds to the detection of a frequency shift xcex94f at the EM pulse signal frequency that is due to a reflection from a target. Accordingly, doppler information is generated for a given range value by subjecting the time series obtained for that range value to comb filter processing, filter bank processing or FFT (Fast Fourier Transform) processing. The azimuth data is conventionally obtained by digital beamforming. More specifically, the radar data at a given range cell and a given doppler cell is weighted by a complex exponential for each antenna element of the receiving antenna array and then summed across all antenna elements. The phase of the complex exponential is related to an azimuth angle, the antenna element spacing and the wavelength of the transmitted EM pulses as is well known to those skilled in the art. Beamforming gives the appearance that the antenna array is tuned to a certain region of the surveillance area defined by the azimuth value used in the complex exponential weights. In this fashion, many beams may be formed to simultaneously cover the entire surveillance area.
To determine a target""s range, azimuth and velocity, a detector processes the generated range, azimuth and doppler information for a given CIT. In general, the detector looks for peaks at a given cell (i.e. a data value or pixel) in a two-dimensional plot known as a range-doppler plot. Target detection usually comprises comparing the amplitude in a given cell with the average amplitude in neighboring cells. The detected targets are then forwarded to a plot extractor which filters the detected targets to reject all of those detections that do not conform to the range, doppler and azimuth properties that are expected for a true target. These filtered targets are then forwarded to a tracker which associates successive detections of a given target to form a track for the target. In this fashion, the movement of a detected target may be tracked throughout the surveillance area.
The detection process is hindered by the addition of noise, which includes the clutter previously mentioned, in each cell. This may result in the missed detection of a target or the false detection of noise as a target. The noise is problematic since there will be a varying noise level in different cells as well as for radar data collected in different CITs, in different sea-state conditions, during different times of day and season and at different locations. The major sources of radar noise include self-interference, such as ocean clutter, ionospheric clutter and meteoroid clutter, and external interference such as co-channel interference, atmospheric interference and impulsive noise. Self-interference results from the operation of the radar while external interference is independent of radar operation.
Ionospheric clutter is one of the most significant causes of interference and is difficult to suppress due to its target-like nature and high signal amplitude. Ionospheric clutter includes EM pulses that reflect off of the earth""s ionosphere and return directly to the radar (i.e. near vertical incidence clutter), and EM pulses that bounce off of the ionosphere, reflect from the ocean and return to the radar along the reverse path (i.e. sky-wave self-interference clutter also referred to as range-wrap clutter). In general, ionospheric clutter accumulates in an annular band spanning narrow bands of range cells, all azimuth cells and most of the ship doppler band. This narrow band of range cells corresponds to the height or multiple heights of the ionospheric layers relative to the HFSWR installation site. Near vertical incidence ionospheric clutter is also characterized as being very strong, isolated in range and smeared in the doppler dimension over many milli-Hertz. During the night, ionospheric clutter is at its highest level due to the disappearance of the ionospheric D layer and the merging of the ionospheric F1 and F2 layers. Furthermore, the characteristics of ionospheric clutter vary with season and other environmental parameters so it is not easy to introduce a robust method to suppress ionospheric noise.
To combat range-wrap clutter, Frank complementary codes may be used as is known to those skilled in the art. Another known solution is to operate the radar system at a higher frequency that does not support sky-wave propagation. By increasing the carrier frequency of the transmitted EM pulses above the layer-critical frequency, the transmitted EM pulses will penetrate through the ionospheric layers. However, this approach may decrease the performance of the radar system in detecting ships at long range due to the greater propagation loss that is incurred at higher transmission frequencies.
The sea surface comprises a number of waves having different wavelengths and amplitudes. Ocean clutter results from EM pulses that are reflected by ocean waves that are harmonics of the radar wavelength. Two large peaks that dominate ocean clutter are referred to as Bragg lines which appear as two columns of peaks in a range-doppler plot along all range cells at doppler frequencies determined by the radar operating frequency. The Bragg lines can smear radar detection performance at their corresponding doppler frequencies. However, there is also higher order scatter, related to the sea-state, that results in additional peaks and a continuum of ocean clutter between the Bragg lines. This continuum of ocean clutter contains energy that is related to the sea-state (i.e. surface wind speed and duration) and often limits the detection of small, low-speed targets such as ships. In addition, the ocean clutter has shown very poor spatial correlation.
Meteoroid clutter results from meteoroids which are small meteor particles that penetrate the Earth""s atmosphere and generate ionization trails that produce transient radar returns. A transient meteoroid radar return usually appears as a large peak at a specific range. Meteoroid clutter results in an increase of the background noise level in range-doppler plots.
Co-channel interference results from both local and distant users of the HFSWR frequency band, such as television broadcasters. This interference has directionality since it originates from spatially correlated sources. However, due to multiple reflections in non-uniform ionospheric layers, the direction of arrival of co-channel interference is wide as can be seen from radar data with co-channel interference shown in FIG. 1. Co-channel interference is also range independent and occurs at specific doppler frequency ranges as can be seen from another sample of radar data shown in FIG. 2. Co-channel interference may be avoided by choosing alternate carrier frequencies for transmitting the EM pulses. However, co-channel interference from distant sources poses a more serious problem since this interference is random in time and frequency. Furthermore, there is typically greater co-channel interference at night than during the day due to the lack of D layer absorption during the night.
Atmospheric interference is spatially white with a level that varies as a function of frequency, time of day, season and geographical location. For instance, the noise level due to atmospheric interference at the lower end of the HF band, increases about 20 dB during the night in comparison with daytime levels.
Impulsive noise is due to lightning and manifests itself as a sequence of rapid pulses that are randomly distributed in time and have an amplitude with a large dynamic range. This can be seen in FIG. 3 which shows a sequence of radar returns plotted versus transmitted EM pulse number (or pulse index) for a given range value. Impulsive noise, shown in FIG. 4, is not spatially white and results from both local and distant storms. Impulsive noise usually occurs throughout the daily operation of an HFSWR system. Impulsive noise results in an increase in the background noise level. The frequency characteristics of impulsive noise change as a function of the intensity of local storm activity.
Needless to say, detection is a very important part of a radar system, and is compromised by the various types of noise described above. Accordingly, to improve detection, these various forms of noise must be suppressed preferably before or during the generation of the range-doppler plot (i.e. spectral estimation) on which detection is usually performed.
For instance, it is known in the prior art that detection in these various forms of interference can be improved by distributing the interference energy over a larger number of cells in the range-doppler plot. This is achieved by improving the range, doppler or azimuth resolution during spectral estimation. However, range resolution is determined by the bandwidth of the transmitted signal and is usually restricted whereas doppler resolution is determined by the CIT which is also limited. Furthermore, azimuth resolution is limited by the aperture size of the receiving antenna array (i.e. the physical size of the receiving antenna array).
One technique that has been introduced to circumvent these restrictions on resolution is the use of high-resolution spectral estimators to increase azimuthal resolution. However, a statistically robust estimation of the covariance matrix of the radar data is required to obtain good results. The estimation of the covariance matrix should also compensate for the various types of radar noise while enhancing the signal contribution to the covariance matrix estimate. Failure to achieve this will result in a range-doppler plot in which the noise will obscure the target and compromise target detection. Accordingly, there is a need for a high-resolution spectral estimator that is statistically robust, can suppress noise and enhance possible targets in the radar data.
Other prior art noise suppression methods have been directed towards external interference cancellation techniques by exploiting the directional characteristics of external interference signals. These techniques employ a main antenna or a main antenna array to obtain radar data for potential targets and external interference, and an auxiliary antenna or an auxiliary antenna array to estimate the external interference only. However, these methods require additional hardware. Specifically, these methods require an auxiliary antenna or an auxiliary antenna array. One prior art solution to this problem involves using a receiving antenna array in which some of the array elements are used as the main antenna array and some of the array elements are used as the auxiliary array. However, this results in a main antenna array having a smaller aperture which degrades azimuthal resolution. Accordingly, there is a need for a method of suppressing external interference without requiring the additional hardware of an auxiliary antenna array and without degrading the azimuthal resolution of the main antenna array.
Other challenges in radar detection include properties that vary across targets such as target type and target velocity. For instance, surface targets, such as ships, appear larger on a range-doppler plot than air targets, such as planes, for a given range and doppler resolution. In addition, air targets are usually much faster than surface targets. This is important since a target whose radial velocity changes within a given CIT has spectral components that are smeared over several Doppler bins. Accordingly, there is also a need for signal processing methods which recognize that targets have varying properties and adapt based on these properties to enhance the appearance of targets on a range-doppler plot.
The inventors of the present invention have developed several embodiments of a system and method of spectral generation and noise suppression to produce range-doppler plots in which potential targets are enhanced. The inventors have developed the systems and methods of the present invention based on the fact that different classes of targets provide different radar signatures on a range-doppler plot although each radar signature has a peaked shape. Furthermore, targets are statistically independent of the various forms of clutter and have radar signatures with strong spatial correlation provided that the signal-to-clutter ratio is sufficient. In addition, the inventors have recognized that various forms of clutter have varying degrees of spatial correlation. For instance ocean clutter (first and higher order) mostly has poor spatial correlation whereas ionospheric clutter has strong spatial correlation.
The inventors have determined that a high-resolution spectral estimator that separates the radar data into signal and noise subspaces may be used to suppress ocean clutter since the ocean clutter appears mainly in the noise subspace due to poor spatial correlation. Furthermore, if the azimuth of ionospheric clutter is different than the azimuth of a possible target, then the high-resolution spectral estimator should be able to distinguish between the ionospheric clutter and the radar signature from the possible target because of the absence of sidelobes in high-resolution spectral estimators. However, robust covariance matrix estimation is needed in order for the high-resolution spectral estimator to enhance radar signatures from targets. The inventors have based covariance matrix estimation on a weighted average of the covariance matrices of range-doppler cells that are in a neighborhood of the range-doppler cell for which a high-resolution spectral vector is generated. The high resolution spectral estimator uses at least a portion of the noise subspace to form a high resolution spatial estimate.
One disadvantage of conventional subspace-based spectral estimators is obtaining good results at low signal-to-clutter (SCR) ratios. The inventors have found that one solution is to reduce the noise subspace dimension by involving only those eigenvectors that correspond to smaller singular values and hence lie more toward spatially white noise. Another approach is to incorporate spatial smoothing on the covariance matrix estimate that is used by the subspace-based spectral estimator to generate a high-resolution spatial vector. The spatial smoothing may be based on any one of forward spatial smoothing, backward spatial smoothing or forward/backward spatial smoothing.
As mentioned previously, another important class of interference is external interference. The inventors of the present invention have developed a module and a method based on a combination of adaptive array processing and matched/mis-matched filtering that is used to suppress external interference. The module may be combined with the spectral generator to provide enhanced high-resolution range-doppler plots. The data recorded by a main sensor array is communicated to matched and mis-matched filter modules in the noise suppression module. The matched filter module provides matched radar data that contains radar returns from possible targets, self-interference and external interference while the mis-matched filter module provides mis-matched radar data that contains only external interference. Accordingly, a virtual auxiliary sensor array may be constructed based on the mis-matched radar data to provide radar data to an adaptive beamformer. The adaptive beamformer preferably generates Wiener-based weights that are applied to the mis-matched radar data to produce an auxiliary beam that provides an estimate of the external interference in the matched radar data for each sensor in the main sensor array that recorded radar data. The external interference estimate is then removed from the recorded radar data for each of the aforementioned sensors to provide noise suppressed range-doppler-sensor radar data. This data may then be provided to the spectral generator of the present invention to produce high-resolution range-doppler plots in which external interference has been suppressed.
Accordingly, in one aspect, the present invention is a spectral generator for radar which receives pre-processed range-doppler-sensor data and generates at least one noise-reduced high-resolution spectrum. The spectral generator comprises a window generator that generates a window which defines a plurality of range-doppler cells. The spectral generator further comprises a covariance matrix calculator that is in communication with the window generator. The covariance matrix calculator receives the range-doppler-sensor data and calculates a covariance matrix estimate for a range-doppler cell of interest in the window. The covariance matrix estimate is calculated from covariance matrices calculated for at least a portion of the plurality of range-doppler cells in the window that are around the range-doppler cell of interest. The spectral generator also includes a spectral calculator that is in communication with the covariance matrix calculator to calculate a high-resolution spectral vector based on a location matrix and a noise subspace matrix estimate.
The spectral generator may further include a covariance matrix smoother that is in communication with the covariance matrix calculator. The covariance matrix smoother smoothes the covariance matrix estimate.
The spectral generator may further include a noise suppression module that is in communication with the covariance matrix calculator to provide noise suppressed radar data instead of the pre-processed range-doppler-sensor data to the covariance matrix calculator. The noise suppression module estimates external interference in the pre-processed radar data and suppresses the external interference in the pre-processed radar data to produce the noise suppressed radar data.
In another aspect, the present invention provides a method of spectral generation for radar. The method comprises:
a) generating a window that defines a plurality of range-doppler cells;
b) calculating a covariance matrix estimate for a range-doppler cell of interest in the window from pre-processed range-doppler-sensor data, wherein the covariance matrix estimate is generated from covariance matrices calculated for at least a portion of the plurality of range-doppler cells within the window; and,
c) calculating a high-resolution spectral vector based on a location matrix and a noise subspace matrix estimate. The noise subspace matrix estimate is derived from the covariance matrix estimate.
The spectral generation method may further comprise smoothing the covariance matrix estimate and calculating the noise subspace matrix based on the smoothed covariance matrix estimate.
The spectral generation method may further comprise a step of suppressing noise to provide noise suppressed radar data instead of the pre-processed range-doppler-sensor data. The step of suppressing noise is effected by estimating external interference in the pre-processed radar data and suppressing the external interference in the pre-processed radar data to produce the noise suppressed radar data.
In another aspect, the present invention provides a noise suppression module for suppressing external interference in pre-processed radar data. The noise suppression module comprises a first processing module and a second processing module that are in communication with the pre-processed radar data. The first processing module receives the pre-processed radar data and produces matched radar data whereas the second processing module receives the pre-processed radar data and produces mis-matched radar data. The noise suppression module further comprises an adaptive beamformer that is in communication with the first processing module and the second processing module. The adaptive beamformer receives a portion of matched radar data and a portion of mis-matched radar data, and produces an external interference estimate of the external interference in the portion of matched radar data. The noise suppression module further comprises a suppressor that is in communication with the first processing module and the adaptive beamformer. The suppressor provides a portion of the noise suppressed radar data based on the portion of matched radar data and the external interference estimate.
The noise suppression module may further comprise an ordered-statistics filter module that is in communication with the first processing module. The ordered-statistics filter module produces ordered-statistics filtered matched radar data.
In a further aspect, the present invention provides a method of suppressing external interference in pre-processed radar data. The method comprises:
a) processing the pre-processed radar data to produce matched radar data;
b) processing the pre-processed radar data to produce mis-matched radar data;
c) selecting a portion of the matched radar data and a portion of the mis-matched radar data and performing adaptive beamforming to produce an estimate of the external interference in the portion of matched radar data; and,
d) producing a portion of the noise suppressed radar data by suppressing the external interference estimate from the portion of matched radar data.
The method may further comprise performing ordered-statistics filtering on the matched radar data to produce ordered-statistics filtered matched radar data.